Global exponential stability analysis of neural networks with an interval time-varying delay via an extended free-matrix-based double integral inequality

被引：0
|
作者：
Zhang, Fen [1 ]
Li, Zhi [2 ]
Zhang, Yanbang [1 ]
机构：
[1] Xianyang Normal Univ, Coll Math & Informat Sci, Xianyang 712000, Shaanxi, Peoples R China
[2] Xidian Univ, Dept Automat Control, Xian 710071, Shaanxi, Peoples R China
来源：
2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC) | 2021年
关键词：
DISCRETE; SYNCHRONIZATION; CRITERIA;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the paper, we study exponential stability of neural networks with an interval time-varying delay. Firstly, we propose an extended free-matrix-based double integral inequality (FMDI) to estimate the double integral terms in the derivative of Lyapunov-Krasovskii functional (LKF). Secondly, we compare the extended FMDI and the FMDI and show that the former encompasses the latter as a special case. Finally, to show the advantages of the extended FMDI over Wirtinger-based double integral inequality (WBDI), we use the two different inequalities to investigate the exponential stability of delayed neural networks and derive new exponential stability criteria based on the same LKF. Moreover, the conservatism comparison of the criteria are illustrated through one numerical example.
引用
收藏
页码：1032 / 1037
页数：6
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